Approaches for Geometry Problem Solving. Due to the sparsity of appropriate data, most early works on automated geometry systems focus on geometry theorem proving [1]}, [2]}, [3]}, [4]}, problem synthesis [5]}, diagram parsing [6]}, as well as problem formalization [7]}. [8]} attempt using computer vision and natural language processing techniques to solve geometry problems with problem understanding.
However, the system does not perform explicit reasoning with axiomatic knowledge as it reduces the task to an optimization problem to see which choice can satisfy all constraints. Some recent efforts [9]}, [10]} have been made to incorporate theorem knowledge into problem solving. They feed geometry axioms written as horn clause rules and declarations from the diagram and text parser into logical programs in prolog style to solve the problem. However, these methods fail to provide human-readable solving steps. And parameter learning on horn clause rules and built-in solvers leads to an uncontrollable search process. In contrast, our proposed Inter-GPS implements explicit symbolic reasoning to infer the answer without the help of candidate answers in an interpretable way.
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